imansingh_Project3_VariablesAnalysis2
Iman Singh
11/2/2017
#library(data.table)
library(ggplot2)
library(gridExtra)
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## %in%, table#library(lsr)
library(corrplot)## corrplot 0.84 loadedlibrary(dplyr)##
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#library(RANN)
library(reshape2)##
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## dcast, melthouse_train <- read.csv("~/Documents/NYC Data Science Academy/Project_3_Kaggle/train.csv",
header = TRUE,
na.strings = c("", "NA"),
stringsAsFactors = TRUE)house_sub <- house_train[,c(3,13,17,18,20,28,30,31,44,45,47,50,55,61,62,63,64,65,66,79,80,81)]
house_sub$SalePriceCat <- as.factor(as.numeric(cut_number(house_sub$SalePrice, 10)))variable_list = c('MSZoning','Neighborhood','HouseStyle','OverallQual','YearBuilt','ExterQual','Foundation','BsmtQual','X1stFlrSF','X2ndFlrSF','GrLivArea','FullBath','TotRmsAbvGrd','GarageFinish','GarageCars','GarageArea','GarageQual','GarageCond','PavedDrive','SaleType','SaleCondition')
numeric_variables = variable_list[c(4,5,9,10,11,12,13,15,16)]
ordinal_variables = variable_list[c(6,8,14,17,18,19)]## histogram on SalePrice
grid.arrange(ggplot(house_train) +
geom_histogram(aes(SalePrice), bins = 20),
ggplot(house_train) +
geom_histogram(aes(log(SalePrice + 1)), bins = 20),
ncol = 2)
table plot all features on sortded SalePrice
colMtx <- matrix(names(house_sub)[1:length(house_sub)-1], nrow = 6)## Warning in matrix(names(house_sub)[1:length(house_sub) - 1], nrow = 6):
## data length [22] is not a sub-multiple or multiple of the number of rows
## [6]for (i in 1:ncol(colMtx)) {
tableplot(house_train,
select_string = c(colMtx[,i], "SalePrice"),
sortCol = "SalePrice", decreasing = TRUE,
nBins = 30)
}
Correlations between Continuous Variables
numeric_features <- names(house_sub)[sapply(house_sub, is.numeric)]
print(numeric_features)## [1] "OverallQual" "YearBuilt" "X1stFlrSF" "X2ndFlrSF"
## [5] "GrLivArea" "FullBath" "TotRmsAbvGrd" "GarageCars"
## [9] "GarageArea" "SalePrice"corr_numtran <- cor(house_sub %>%
select(one_of(numeric_features, "SalePrice")),
method = "pearson",
use = "pairwise.complete.obs")
corrplot(corr_numtran, method = "color", order="hclust")
cor(house_sub$FullBath, house_sub$SalePrice)## [1] 0.5606638attach(house_sub)
cor(OverallQual, SalePrice)## [1] 0.7909816cor(YearBuilt, SalePrice)## [1] 0.5228973cor(X1stFlrSF, SalePrice)## [1] 0.6058522cor(X2ndFlrSF, SalePrice)## [1] 0.3193338cor(GrLivArea, SalePrice)## [1] 0.7086245cor(FullBath, SalePrice)## [1] 0.5606638cor(TotRmsAbvGrd, SalePrice)## [1] 0.5337232cor(GarageCars, SalePrice)## [1] 0.6404092cor(GarageArea, SalePrice)## [1] 0.6234314numeric_df <- house_sub %>% select(numeric_features)
numeric_melt <- melt(numeric_df)## No id variables; using all as measure variablesggplot(data = numeric_melt, mapping = aes(x = value)) +
geom_histogram(bins = 10) + facet_wrap(~variable, scales = 'free_x')
g <- ggplot(house_sub, aes(y=SalePrice))
g + geom_point(aes(x=OverallQual))
g + geom_point(aes(x=YearBuilt))
g + geom_point(aes(x=X1stFlrSF))
g + geom_point(aes(x=X2ndFlrSF))
g + geom_point(aes(x=GrLivArea))
g + geom_point(aes(x=FullBath))
g + geom_point(aes(x=TotRmsAbvGrd))
g + geom_point(aes(x=GarageCars))
g + geom_point(aes(x=GarageArea))
Correlations for Ordinal Values
## ordinal features are those who contain one of the follow levels
corr_ordtran <- cor(data.matrix(house_sub %>%
select(one_of(ordinal_variables))),
method = "kendall",
use = "pairwise.complete.obs")
corrplot(corr_ordtran, method = "color", order="hclust")
n_fun <- function(x){
return(data.frame(y = median(x), label = paste0("n = ",length(x))))
}
g0 <- ggplot(data = house_sub, aes(x = MSZoning))
g0 + geom_bar()
g0 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g1 <- ggplot(data = house_sub, aes(x = Neighborhood))
g1 + geom_bar() + coord_flip()
g1 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = .9), fun.data = n_fun, geom = "text") + coord_flip()
g2 <- ggplot(data = house_sub, aes(x = HouseStyle))
g2 + geom_bar()
g2 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g3 <- ggplot(data = house_sub, aes(x = ExterQual))
g3 + geom_bar()
g3 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g4 <- ggplot(data = house_sub, aes(x = Foundation))
g4 + geom_bar()
g4 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g5 <- ggplot(data = house_sub, aes(x = BsmtQual))
g5 + geom_bar()
g5 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g6 <- ggplot(data = house_sub, aes(x = GarageFinish))
g6 + geom_bar()
g6 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g7 <- ggplot(data = house_sub, aes(x = GarageQual))
g7 + geom_bar()
g7 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g8 <- ggplot(data = house_sub, aes(x = GarageCond))
g8 + geom_bar()
g8 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g9 <- ggplot(data = house_sub, aes(x = PavedDrive))
g9 + geom_bar()
g9 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g10 <- ggplot(data = house_sub, aes(x = SaleType))
g10 + geom_bar()
g10 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g11 <- ggplot(data = house_sub, aes(x = SaleCondition))
g11 + geom_bar()
g11 + geom_bar(aes(fill = SalePriceCat), position = 'fill') + scale_color_brewer("RdYlGn") + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
#n_fun <- function(x){
# return(data.frame(y = median(x), label = paste0("n = ",length(x))))
#}
g0 <- ggplot(data = house_sub, aes(x = MSZoning, y=SalePrice))
g0 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g1 <- ggplot(data = house_sub, aes(x = Neighborhood, y=SalePrice))
g1 + geom_boxplot() + stat_summary(aes(y = .9), fun.data = n_fun, geom = "text") + coord_flip()
g2 <- ggplot(data = house_sub, aes(x = HouseStyle, y=SalePrice))
g2 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g3 <- ggplot(data = house_sub, aes(x = ExterQual, y=SalePrice))
g3 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g4 <- ggplot(data = house_sub, aes(x = Foundation, y=SalePrice))
g4 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g5 <- ggplot(data = house_sub, aes(x = BsmtQual, y=SalePrice))
g5 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g6 <- ggplot(data = house_sub, aes(x = GarageFinish, y=SalePrice))
g6 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g7 <- ggplot(data = house_sub, aes(x = GarageQual, y=SalePrice))
g7 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g8 <- ggplot(data = house_sub, aes(x = GarageCond, y=SalePrice))
g8 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g9 <- ggplot(data = house_sub, aes(x = PavedDrive, y=SalePrice))
g9 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g10 <- ggplot(data = house_sub, aes(x = SaleType, y=SalePrice))
g10 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
g11 <- ggplot(data = house_sub, aes(x = SaleCondition, y=SalePrice))
g11 + geom_boxplot() + stat_summary(aes(y = 1.05), fun.data = n_fun, geom = "text")
Missingness
Categorical Values
sum(is.na(MSZoning))## [1] 0sum(is.na(Neighborhood))## [1] 0sum(is.na(HouseStyle))## [1] 0sum(is.na(OverallQual))## [1] 0sum(is.na(YearBuilt))## [1] 0sum(is.na(ExterQual))## [1] 0sum(is.na(Foundation))## [1] 0sum(is.na(BsmtQual))## [1] 37sum(is.na(X1stFlrSF))## [1] 0sum(is.na(X2ndFlrSF))## [1] 0sum(is.na(GrLivArea))## [1] 0sum(is.na(FullBath))## [1] 0sum(is.na(TotRmsAbvGrd))## [1] 0sum(is.na(GarageFinish))## [1] 81sum(is.na(GarageCars))## [1] 0sum(is.na(GarageArea))## [1] 0sum(is.na(GarageQual))## [1] 81sum(is.na(GarageCond))## [1] 81sum(is.na(PavedDrive))## [1] 0sum(is.na(SaleType))## [1] 0sum(is.na(SaleCondition))## [1] 0## check missing values
col_missing <- names(house_sub)[colSums(is.na(house_sub)) > 0]
aggr(house_sub[,col_missing], prop = F, numbers = T)
Filter(function(x) x > 0, colSums(is.na(house_sub)))## BsmtQual GarageFinish GarageQual GarageCond
## 37 81 81 81sum(OverallQual < (quantile(OverallQual, .25) - 1.5*IQR(OverallQual)))## [1] 2sum(OverallQual > (quantile(OverallQual, .75) + 1.5*IQR(OverallQual)))## [1] 0sum(YearBuilt < (quantile(YearBuilt, .25) - 1.5*IQR(YearBuilt)))## [1] 7sum(YearBuilt > (quantile(YearBuilt, .75) + 1.5*IQR(YearBuilt)))## [1] 0sum(X1stFlrSF < (quantile(X1stFlrSF, .25) - 1.5*IQR(X1stFlrSF)))## [1] 0sum(X1stFlrSF > (quantile(X1stFlrSF, .75) + 1.5*IQR(X1stFlrSF)))## [1] 20sum(X2ndFlrSF < (quantile(X2ndFlrSF, .25) - 1.5*IQR(X2ndFlrSF)))## [1] 0sum(X2ndFlrSF > (quantile(X2ndFlrSF, .75) + 1.5*IQR(X2ndFlrSF)))## [1] 2sum(GrLivArea < (quantile(GrLivArea, .25) - 1.5*IQR(GrLivArea)))## [1] 0sum(GrLivArea > (quantile(GrLivArea, .75) + 1.5*IQR(GrLivArea)))## [1] 31sum(FullBath < (quantile(FullBath, .25) - 1.5*IQR(FullBath)))## [1] 0sum(FullBath > (quantile(FullBath, .75) + 1.5*IQR(FullBath)))## [1] 0sum(TotRmsAbvGrd < (quantile(TotRmsAbvGrd, .25) - 1.5*IQR(TotRmsAbvGrd)))## [1] 0sum(TotRmsAbvGrd > (quantile(TotRmsAbvGrd, .75) + 1.5*IQR(TotRmsAbvGrd)))## [1] 30sum(GarageCars < (quantile(GarageCars, .25) - 1.5*IQR(GarageCars)))## [1] 0sum(GarageCars > (quantile(GarageCars, .75) + 1.5*IQR(GarageCars)))## [1] 5sum(GarageArea < (quantile(GarageArea, .25) - 1.5*IQR(GarageArea)))## [1] 0sum(GarageArea > (quantile(GarageArea, .75) + 1.5*IQR(GarageArea)))## [1] 21Categorical Values
apply(house_sub, MARGIN = 2, table)## $MSZoning
##
## C (all) FV RH RL RM
## 10 65 16 1151 218
##
## $Neighborhood
##
## Blmngtn Blueste BrDale BrkSide ClearCr CollgCr Crawfor Edwards Gilbert
## 17 2 16 58 28 150 51 100 79
## IDOTRR MeadowV Mitchel NAmes NoRidge NPkVill NridgHt NWAmes OldTown
## 37 17 49 225 41 9 77 73 113
## Sawyer SawyerW Somerst StoneBr SWISU Timber Veenker
## 74 59 86 25 25 38 11
##
## $HouseStyle
##
## 1.5Fin 1.5Unf 1Story 2.5Fin 2.5Unf 2Story SFoyer SLvl
## 154 14 726 8 11 445 37 65
##
## $OverallQual
##
## 1 2 3 4 5 6 7 8 9 10
## 2 3 20 116 397 374 319 168 43 18
##
## $YearBuilt
##
## 1872 1875 1880 1882 1885 1890 1892 1893 1898 1900 1904 1905 1906 1908 1910
## 1 1 4 1 2 2 2 1 1 10 1 1 1 2 17
## 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925
## 1 3 1 7 10 8 1 7 3 30 6 8 7 7 16
## 1926 1927 1928 1929 1930 1931 1932 1934 1935 1936 1937 1938 1939 1940 1941
## 9 3 7 4 9 6 4 3 6 9 5 4 8 18 15
## 1942 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958
## 2 6 7 5 14 12 20 6 5 12 24 16 14 20 24
## 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973
## 26 17 14 19 16 15 24 18 16 22 14 24 22 23 11
## 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988
## 10 8 33 32 16 9 10 5 6 4 9 5 5 3 11
## 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
## 3 12 5 13 17 19 18 15 14 25 25 24 20 23 45
## 2004 2005 2006 2007 2008 2009 2010
## 54 64 67 49 23 18 1
##
## $ExterQual
##
## Ex Fa Gd TA
## 52 14 488 906
##
## $Foundation
##
## BrkTil CBlock PConc Slab Stone Wood
## 146 634 647 24 6 3
##
## $BsmtQual
##
## Ex Fa Gd TA
## 121 35 618 649
##
## $X1stFlrSF
##
## 334 372 438 480 483 495 520 525 526 536 546 551 561 572 575
## 1 1 1 1 7 1 5 1 1 1 3 1 1 2 1
## 576 581 596 600 605 612 616 624 625 626 630 649 658 660 661
## 1 1 1 2 1 2 5 2 2 1 9 1 1 2 1
## 663 664 672 673 676 679 680 682 684 686 689 691 693 694 696
## 1 1 11 1 1 1 1 1 3 1 2 2 1 3 1
## 697 698 702 703 707 708 713 716 720 725 728 729 734 735 736
## 2 5 1 1 2 2 1 2 6 2 6 3 2 2 1
## 738 741 742 747 750 751 752 753 754 755 756 757 760 764 765
## 1 1 3 2 1 1 1 1 1 1 4 2 1 6 2
## 767 768 769 770 772 773 774 778 779 780 783 784 786 788 789
## 1 5 2 1 1 3 5 2 2 5 1 2 2 2 2
## 790 792 793 794 796 798 799 800 802 803 804 806 807 808 810
## 2 3 1 1 5 5 1 2 2 2 5 1 2 3 4
## 811 812 813 814 815 816 818 820 822 824 825 827 829 831 832
## 2 1 1 1 1 9 1 2 1 1 2 2 1 1 7
## 833 835 838 840 841 842 845 846 847 848 849 851 854 855 856
## 3 1 1 6 2 1 2 1 4 12 1 1 2 4 1
## 858 859 860 861 864 865 866 869 872 874 875 876 877 879 880
## 5 1 4 1 25 1 4 2 2 3 1 2 1 1 2
## 882 884 885 886 887 888 889 892 893 894 896 897 899 900 901
## 6 4 1 1 1 3 1 4 1 12 2 1 1 4 3
## 902 904 905 907 908 909 910 912 913 914 915 916 918 920 923
## 4 3 1 1 1 1 3 14 1 1 1 3 3 2 2
## 924 925 926 927 928 929 930 932 933 935 936 938 939 941 943
## 1 2 2 1 4 1 1 2 1 1 7 2 2 1 3
## 944 948 949 950 951 952 953 954 955 956 958 959 960 961 962
## 3 5 1 2 1 4 2 3 2 2 5 2 7 2 1
## 963 964 965 966 968 969 970 971 972 974 975 976 977 979 980
## 1 2 1 1 3 1 4 1 2 1 1 3 1 3 5
## 981 983 984 985 986 988 989 990 991 992 993 996 997 998 999
## 2 3 1 1 1 5 1 6 1 1 2 2 3 1 3
## 1001 1002 1003 1004 1005 1006 1007 1008 1010 1012 1013 1014 1015 1020 1021
## 2 2 1 4 3 3 1 3 1 2 1 2 1 2 1
## 1022 1024 1026 1028 1029 1032 1034 1035 1036 1038 1039 1040 1041 1042 1044
## 4 1 5 2 1 3 1 1 1 1 1 16 2 1 1
## 1047 1048 1050 1051 1052 1053 1054 1055 1056 1057 1058 1060 1061 1062 1063
## 1 4 4 1 3 4 2 2 6 4 1 3 1 2 1
## 1064 1065 1067 1068 1069 1071 1072 1073 1074 1075 1077 1078 1079 1080 1082
## 1 2 2 1 2 1 5 2 1 1 3 2 2 5 2
## 1085 1086 1088 1089 1090 1091 1092 1094 1095 1096 1097 1098 1099 1100 1103
## 1 4 5 1 1 1 4 2 3 3 3 3 1 1 1
## 1104 1105 1107 1108 1110 1112 1113 1114 1116 1117 1118 1120 1121 1122 1124
## 1 1 1 2 1 3 3 2 1 1 2 5 2 2 1
## 1125 1126 1127 1128 1130 1131 1132 1133 1134 1136 1137 1138 1140 1141 1142
## 2 3 2 4 2 1 2 2 3 4 2 1 2 1 3
## 1143 1144 1145 1146 1148 1149 1150 1152 1153 1154 1155 1156 1158 1159 1160
## 1 5 2 1 3 2 1 3 2 1 1 1 2 1 2
## 1161 1163 1164 1165 1166 1167 1168 1170 1172 1173 1175 1178 1180 1181 1182
## 1 2 4 2 3 2 2 1 1 1 2 2 2 1 2
## 1184 1186 1187 1188 1190 1192 1194 1195 1196 1199 1200 1203 1204 1207 1208
## 1 1 1 4 2 1 2 1 2 1 5 1 1 1 3
## 1210 1211 1212 1214 1215 1216 1217 1218 1220 1221 1222 1223 1224 1225 1226
## 1 1 3 3 1 3 1 1 3 2 2 1 3 1 2
## 1228 1229 1232 1234 1235 1236 1238 1240 1241 1242 1244 1246 1247 1248 1249
## 2 1 2 2 1 5 1 1 1 1 1 2 1 1 1
## 1251 1252 1253 1256 1258 1260 1261 1262 1264 1265 1266 1268 1269 1272 1274
## 1 3 2 1 3 1 1 2 2 1 2 3 2 1 1
## 1276 1277 1279 1281 1282 1283 1284 1285 1287 1288 1291 1294 1296 1297 1298
## 3 2 1 1 1 1 1 1 1 1 2 2 2 1 2
## 1299 1301 1302 1304 1306 1307 1309 1310 1314 1316 1318 1319 1320 1324 1325
## 1 3 3 1 4 1 1 2 5 2 1 1 2 2 1
## 1327 1328 1332 1334 1336 1337 1338 1339 1340 1342 1344 1349 1350 1352 1357
## 1 4 1 1 1 4 2 2 2 1 3 1 2 3 1
## 1358 1360 1361 1362 1363 1364 1368 1370 1372 1375 1377 1378 1381 1382 1383
## 2 3 1 3 2 1 2 3 2 1 1 2 1 2 3
## 1389 1390 1391 1392 1394 1402 1405 1407 1411 1412 1414 1416 1419 1422 1423
## 1 1 2 5 1 1 1 1 1 1 3 1 1 5 1
## 1425 1426 1428 1429 1430 1431 1432 1434 1436 1437 1440 1442 1444 1445 1453
## 1 1 2 1 1 2 1 1 2 2 4 4 1 2 2
## 1454 1456 1459 1462 1464 1465 1466 1468 1470 1472 1473 1476 1478 1479 1482
## 1 1 1 1 2 1 3 2 1 1 1 2 3 2 2
## 1484 1486 1489 1490 1493 1494 1496 1498 1500 1501 1502 1504 1505 1506 1507
## 3 1 2 1 1 5 2 1 3 1 2 3 1 2 1
## 1509 1512 1516 1518 1520 1521 1523 1525 1526 1530 1532 1533 1535 1536 1537
## 1 1 2 3 1 1 1 1 1 2 1 1 3 1 1
## 1541 1542 1547 1548 1549 1552 1553 1554 1555 1557 1559 1560 1561 1563 1566
## 2 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 1567 1569 1571 1572 1573 1574 1575 1576 1578 1580 1582 1584 1586 1588 1593
## 2 1 1 2 1 1 2 1 1 2 1 2 1 2 1
## 1600 1601 1602 1604 1610 1614 1616 1617 1619 1620 1621 1622 1624 1625 1629
## 2 2 1 1 2 1 2 1 1 2 1 1 1 1 2
## 1630 1632 1634 1636 1640 1644 1646 1647 1651 1652 1654 1656 1657 1659 1660
## 2 2 1 2 1 2 3 1 1 3 1 3 1 1 1
## 1661 1663 1664 1668 1670 1675 1679 1680 1682 1684 1686 1687 1689 1690 1694
## 2 1 2 2 1 1 1 3 1 1 2 2 3 1 3
## 1696 1698 1699 1700 1701 1702 1704 1707 1709 1710 1712 1713 1717 1718 1719
## 1 1 2 2 1 1 1 1 1 2 1 1 1 2 1
## 1720 1721 1724 1726 1728 1733 1734 1742 1746 1752 1766 1768 1771 1776 1779
## 1 1 1 1 6 1 3 2 1 1 2 1 1 2 1
## 1787 1788 1792 1795 1800 1801 1803 1811 1824 1826 1828 1831 1836 1838 1839
## 1 1 1 2 3 1 1 1 1 1 1 1 2 1 1
## 1842 1844 1848 1850 1856 1867 1869 1872 1888 1898 1902 1905 1922 1932 1940
## 1 3 1 1 1 1 1 1 1 1 1 1 1 1 2
## 1944 1959 1966 1968 1973 1976 1980 1987 1992 2000 2018 2020 2028 2036 2042
## 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1
## 2046 2053 2069 2073 2076 2084 2097 2110 2113 2117 2121 2129 2136 2156 2158
## 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## 2196 2207 2217 2223 2234 2259 2364 2392 2402 2411 2444 2515 2524 2633 2898
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3138 3228 4692
## 1 1 1
##
## $X2ndFlrSF
##
## 0 110 167 192 208 213 220 224 240 252 272 299 304 316 319
## 829 1 1 1 1 1 1 1 2 2 1 1 1 1 1
## 325 332 336 348 349 351 358 368 370 371 378 384 390 403 406
## 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2
## 408 430 432 438 439 441 445 448 454 455 456 457 462 464 467
## 2 1 2 1 1 1 2 2 1 1 1 1 1 1 2
## 468 472 473 475 482 495 504 510 511 512 514 517 518 520 521
## 1 2 1 1 1 1 9 1 1 1 1 1 1 2 1
## 523 524 526 527 530 533 534 539 540 545 546 547 548 550 551
## 1 1 1 1 2 1 2 1 1 1 8 1 1 2 4
## 556 557 560 561 564 566 567 568 571 573 574 576 580 581 582
## 1 1 2 1 2 1 2 2 1 1 2 2 1 1 1
## 584 586 587 589 590 591 592 595 596 600 601 602 605 611 612
## 2 1 1 1 1 1 1 1 3 7 2 1 1 1 2
## 620 622 623 625 626 628 630 631 634 636 639 640 644 648 649
## 2 1 1 2 1 1 1 1 1 1 1 2 1 2 1
## 650 651 653 656 660 661 664 665 668 670 672 676 677 678 679
## 2 2 1 2 3 1 1 1 3 3 8 2 3 2 1
## 682 684 685 686 687 688 689 691 694 695 698 700 701 702 703
## 1 3 2 1 1 2 5 1 1 1 2 1 1 4 2
## 704 707 708 709 711 712 713 714 716 717 720 725 727 728 729
## 3 1 1 2 1 1 1 1 2 1 7 1 1 10 3
## 730 732 734 738 739 741 742 743 744 745 748 750 752 754 755
## 1 1 1 1 4 4 3 1 2 1 2 1 1 2 2
## 756 761 762 764 765 766 767 768 769 772 776 778 779 780 783
## 5 1 2 2 2 1 1 1 1 1 1 1 1 5 2
## 784 785 787 788 790 793 795 796 797 798 800 804 806 807 808
## 1 2 1 1 2 3 3 1 2 1 2 4 2 3 2
## 809 811 812 813 817 826 828 829 830 831 832 833 834 836 838
## 2 1 1 2 1 1 1 2 1 1 3 2 1 1 1
## 840 842 843 844 846 848 850 854 855 857 858 860 862 864 866
## 5 2 1 2 3 1 2 1 1 1 3 2 5 2 3
## 868 870 871 872 873 874 875 876 878 880 881 882 883 884 885
## 1 1 3 2 1 1 1 2 4 3 1 1 1 2 2
## 886 887 888 892 895 896 898 900 901 902 903 904 908 910 912
## 3 2 3 1 1 6 1 2 1 2 1 1 1 1 2
## 913 914 915 918 920 924 925 928 929 930 932 933 936 939 940
## 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1
## 941 950 954 956 960 966 971 975 977 978 979 980 981 983 984
## 2 1 1 1 1 1 1 2 1 2 1 1 1 2 1
## 985 988 989 992 994 998 1000 1001 1015 1017 1020 1028 1031 1032 1037
## 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1
## 1038 1039 1040 1044 1051 1053 1054 1060 1063 1066 1067 1070 1072 1080 1081
## 1 1 2 1 1 1 1 1 1 1 1 2 1 1 1
## 1088 1092 1093 1096 1097 1098 1100 1101 1103 1104 1106 1111 1112 1116 1120
## 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1
## 1121 1122 1128 1129 1134 1140 1141 1142 1151 1152 1157 1160 1168 1169 1174
## 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1
## 1175 1177 1178 1182 1185 1194 1196 1200 1203 1208 1215 1216 1218 1230 1237
## 1 2 1 1 1 1 1 1 1 2 2 1 1 1 1
## 1242 1243 1254 1257 1259 1274 1276 1281 1286 1288 1296 1304 1306 1312 1320
## 1 1 3 1 1 1 1 1 1 1 1 1 1 1 2
## 1323 1330 1332 1336 1347 1349 1357 1360 1362 1370 1392 1414 1426 1427 1440
## 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## 1479 1518 1519 1523 1538 1540 1589 1611 1796 1818 1872 2065
## 1 1 1 1 1 1 1 1 1 1 1 1
##
## $GrLivArea
##
## 334 438 480 520 605 616 630 672 691 693 694 698 708 720 729
## 1 1 1 1 1 1 6 2 1 1 1 1 1 3 1
## 747 752 754 764 767 768 773 774 778 780 784 788 789 790 792
## 2 1 1 1 1 3 1 1 1 1 2 1 2 1 1
## 796 800 803 804 813 816 825 827 832 833 835 838 840 841 845
## 2 1 1 1 1 8 1 1 1 2 1 1 1 1 2
## 848 854 858 860 861 864 866 869 872 874 875 882 884 886 892
## 10 1 4 1 1 22 1 1 1 1 1 4 1 1 2
## 893 894 899 900 901 902 904 907 910 912 913 914 918 923 924
## 1 11 1 3 1 2 3 1 1 9 1 1 1 2 3
## 925 928 930 932 935 936 938 943 944 948 950 951 952 954 955
## 1 2 1 1 2 3 1 1 1 3 1 1 3 2 1
## 958 960 964 965 968 969 970 971 972 974 980 981 985 986 987
## 3 5 1 1 3 1 1 1 1 1 3 1 1 1 7
## 988 990 996 999 1002 1003 1004 1005 1006 1008 1012 1015 1020 1022 1026
## 5 5 2 3 1 1 2 1 1 2 1 1 1 1 2
## 1028 1029 1032 1034 1039 1040 1041 1044 1047 1048 1050 1052 1053 1054 1056
## 1 1 1 2 1 14 1 1 1 3 2 2 2 2 6
## 1057 1060 1062 1063 1065 1067 1068 1069 1072 1073 1074 1077 1078 1080 1082
## 2 2 1 1 1 1 1 2 3 2 1 3 2 3 1
## 1086 1088 1090 1092 1094 1095 1096 1097 1098 1099 1100 1102 1103 1108 1109
## 2 1 1 8 1 1 2 2 1 1 1 1 1 1 1
## 1110 1111 1112 1113 1114 1116 1117 1118 1120 1121 1122 1123 1124 1125 1126
## 1 3 1 2 2 1 1 3 2 2 1 1 1 2 3
## 1128 1130 1131 1132 1134 1135 1136 1137 1138 1140 1141 1142 1144 1145 1146
## 3 1 2 1 2 1 1 1 1 2 1 1 5 1 1
## 1148 1150 1152 1154 1155 1158 1159 1163 1164 1165 1166 1167 1173 1176 1178
## 2 1 3 2 1 2 1 1 2 1 1 2 1 2 2
## 1180 1181 1183 1184 1188 1190 1192 1194 1196 1198 1199 1200 1203 1204 1208
## 1 1 1 1 2 1 2 2 3 1 1 9 1 1 4
## 1211 1212 1214 1215 1216 1217 1218 1220 1221 1223 1224 1225 1226 1228 1229
## 1 3 3 1 2 2 5 2 2 1 6 1 1 2 2
## 1230 1232 1234 1235 1236 1240 1241 1242 1244 1246 1247 1248 1250 1251 1252
## 2 2 1 1 3 1 1 1 1 1 1 2 4 1 5
## 1253 1256 1258 1261 1262 1264 1265 1266 1268 1269 1271 1274 1276 1277 1279
## 2 1 4 1 2 1 1 2 3 2 1 1 1 1 1
## 1283 1284 1285 1287 1288 1291 1294 1296 1297 1298 1301 1302 1304 1306 1308
## 1 1 2 1 1 1 3 2 1 1 2 5 1 3 1
## 1309 1310 1314 1316 1317 1319 1320 1322 1324 1327 1328 1334 1336 1337 1338
## 1 2 5 3 1 1 2 1 3 1 3 1 2 4 1
## 1339 1340 1342 1343 1344 1346 1347 1348 1349 1350 1352 1355 1357 1358 1360
## 2 2 1 1 7 1 1 2 1 2 3 2 1 2 3
## 1362 1363 1364 1365 1367 1368 1369 1370 1372 1374 1375 1376 1378 1381 1382
## 4 3 1 2 2 3 1 2 1 1 2 2 1 1 4
## 1383 1385 1386 1387 1389 1391 1392 1393 1394 1395 1396 1400 1402 1405 1406
## 2 1 1 1 2 1 5 1 2 1 1 1 1 1 1
## 1411 1412 1414 1416 1419 1422 1425 1426 1428 1429 1430 1431 1432 1434 1436
## 1 3 4 4 2 3 1 2 2 1 3 2 3 1 1
## 1437 1440 1441 1442 1445 1446 1452 1453 1456 1458 1459 1464 1466 1468 1469
## 2 4 2 4 1 1 2 1 10 2 1 2 3 1 1
## 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1481 1482 1484 1486 1487
## 3 1 3 1 1 1 1 1 3 4 1 3 6 1 1
## 1489 1490 1493 1494 1496 1498 1500 1501 1502 1504 1505 1506 1507 1509 1510
## 3 1 1 6 2 2 3 3 4 3 2 2 1 3 1
## 1511 1512 1513 1516 1517 1518 1520 1522 1523 1524 1525 1526 1529 1530 1533
## 1 1 1 1 2 1 2 1 1 2 2 2 1 2 1
## 1535 1536 1537 1539 1541 1547 1548 1550 1552 1553 1554 1555 1556 1557 1558
## 3 1 1 2 2 1 2 1 2 1 1 2 2 1 2
## 1559 1560 1561 1563 1564 1566 1567 1569 1571 1572 1573 1574 1575 1576 1577
## 1 2 1 1 2 1 2 1 2 2 4 2 1 3 1
## 1578 1580 1582 1584 1586 1588 1590 1593 1595 1600 1601 1602 1603 1604 1605
## 2 1 2 1 1 3 1 3 1 3 3 1 1 3 1
## 1608 1610 1611 1614 1616 1617 1620 1621 1622 1624 1625 1626 1629 1630 1632
## 1 1 1 1 3 1 3 1 1 1 1 3 2 2 4
## 1634 1635 1636 1638 1639 1640 1641 1644 1646 1647 1651 1652 1654 1656 1657
## 1 2 1 1 1 3 1 3 4 3 1 4 1 5 1
## 1658 1659 1660 1661 1663 1664 1665 1666 1668 1670 1671 1674 1675 1679 1680
## 1 1 3 3 1 4 2 1 4 1 1 2 1 1 4
## 1682 1683 1684 1686 1687 1688 1689 1690 1691 1692 1694 1696 1698 1699 1700
## 2 1 1 1 1 1 3 2 1 1 4 1 1 1 2
## 1701 1702 1704 1707 1708 1709 1710 1712 1713 1714 1716 1717 1718 1719 1720
## 1 1 1 1 1 4 5 1 1 1 4 3 3 1 3
## 1721 1724 1725 1726 1728 1732 1733 1734 1738 1739 1740 1742 1743 1744 1746
## 2 2 1 1 7 1 2 4 1 1 1 2 1 1 1
## 1750 1752 1756 1761 1762 1764 1766 1767 1768 1771 1774 1775 1776 1779 1784
## 2 2 1 1 1 1 1 1 6 1 2 1 3 2 1
## 1786 1787 1788 1790 1792 1795 1796 1797 1800 1801 1802 1803 1804 1811 1812
## 3 1 2 2 4 3 2 1 4 2 1 1 1 1 2
## 1818 1820 1824 1826 1828 1829 1836 1838 1839 1840 1842 1844 1845 1846 1848
## 1 2 3 2 1 1 2 2 3 1 1 3 1 1 1
## 1850 1851 1852 1855 1856 1861 1863 1865 1867 1868 1869 1872 1876 1886 1888
## 2 1 2 2 1 1 1 1 1 2 2 2 1 1 1
## 1889 1894 1895 1902 1904 1905 1908 1911 1912 1913 1915 1919 1920 1922 1923
## 1 1 1 2 2 1 2 1 1 1 1 1 3 2 1
## 1924 1928 1929 1932 1933 1935 1936 1939 1940 1944 1947 1948 1949 1950 1953
## 1 3 1 1 1 2 1 1 1 1 1 1 1 1 2
## 1954 1958 1959 1960 1961 1962 1964 1968 1969 1970 1971 1973 1976 1977 1978
## 1 2 3 2 1 1 2 2 1 1 1 1 1 1 1
## 1980 1981 1982 1983 1986 1987 1989 1991 1992 1993 2000 2002 2007 2008 2013
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2018 2019 2020 2021 2022 2028 2030 2031 2034 2035 2036 2042 2046 2054 2057
## 1 1 3 1 1 1 1 2 1 1 1 1 2 1 1
## 2058 2060 2062 2069 2073 2076 2078 2080 2082 2084 2087 2090 2093 2094 2097
## 1 2 1 1 1 1 1 2 1 2 1 3 1 1 3
## 2098 2108 2110 2112 2113 2117 2119 2121 2126 2127 2132 2134 2136 2138 2142
## 1 1 2 1 1 1 1 1 1 2 1 1 1 1 1
## 2144 2149 2153 2156 2157 2158 2161 2167 2169 2172 2183 2184 2192 2196 2198
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2200 2201 2207 2210 2217 2223 2224 2229 2230 2234 2236 2240 2243 2256 2259
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2260 2262 2263 2267 2270 2274 2285 2287 2290 2291 2295 2296 2320 2324 2329
## 1 1 3 1 1 1 1 1 2 2 1 1 2 1 1
## 2332 2337 2340 2344 2345 2353 2358 2364 2365 2372 2374 2376 2380 2392 2398
## 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1
## 2402 2403 2414 2417 2418 2447 2448 2450 2452 2462 2466 2468 2473 2482 2504
## 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1
## 2514 2515 2519 2520 2521 2524 2526 2531 2554 2555 2574 2576 2596 2599 2601
## 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1
## 2610 2612 2614 2620 2622 2624 2630 2633 2634 2640 2643 2646 2654 2668 2696
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2704 2713 2715 2727 2728 2730 2775 2784 2792 2794 2810 2822 2828 2868 2872
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## 2898 2945 2978 3082 3086 3112 3140 3194 3222 3228 3238 3279 3395 3447 3493
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 3608 3627 4316 4476 4676 5642
## 1 1 1 1 1 1
##
## $FullBath
##
## 0 1 2 3
## 9 650 768 33
##
## $TotRmsAbvGrd
##
## 2 3 4 5 6 7 8 9 10 11 12 14
## 1 17 97 275 402 329 187 75 47 18 11 1
##
## $GarageFinish
##
## Fin RFn Unf
## 352 422 605
##
## $GarageCars
##
## 0 1 2 3 4
## 81 369 824 181 5
##
## $GarageArea
##
## 0 160 164 180 186 189 192 198 200 205 208 210 213 216 220
## 81 2 1 9 1 1 1 1 4 3 1 3 1 12 3
## 225 228 230 234 240 244 246 248 250 252 254 255 256 260 261
## 2 1 1 2 38 1 2 1 3 5 1 1 2 3 1
## 264 270 271 273 275 276 280 281 282 283 284 286 287 288 290
## 24 5 2 2 3 2 16 3 2 1 1 9 1 27 1
## 292 294 296 297 299 300 301 303 304 305 306 308 309 312 315
## 1 4 2 5 5 10 2 1 2 2 1 20 1 9 2
## 318 319 320 322 324 325 326 327 328 330 336 338 342 343 349
## 1 1 1 1 1 1 2 1 1 1 12 5 1 1 2
## 350 352 354 358 360 364 366 367 368 370 372 373 375 377 379
## 1 12 1 1 11 3 1 1 2 1 1 1 1 1 1
## 380 384 386 388 389 390 392 393 396 397 398 400 402 403 404
## 6 9 1 3 2 6 3 3 6 3 3 25 4 2 1
## 405 406 408 409 410 412 413 414 416 418 420 422 423 424 425
## 1 1 1 2 3 1 1 1 2 2 19 2 1 1 1
## 426 427 429 430 431 432 433 434 435 436 437 438 439 440 441
## 1 1 3 4 5 8 2 5 1 2 1 3 2 49 2
## 442 444 445 447 450 451 452 453 454 455 456 457 458 459 460
## 4 1 1 2 5 5 3 2 2 1 2 3 1 1 7
## 461 462 463 466 467 468 470 471 472 473 474 475 476 477 478
## 6 10 4 2 2 2 4 3 6 2 7 1 2 1 6
## 479 480 481 482 483 484 486 487 490 492 493 494 495 496 497
## 1 24 1 2 2 34 3 3 6 6 2 1 4 2 2
## 498 499 500 501 502 504 505 506 508 509 510 511 512 513 514
## 2 2 6 1 4 12 3 7 3 1 1 2 2 2 3
## 515 516 518 520 521 522 523 525 526 527 528 529 530 531 532
## 1 4 1 8 2 2 1 6 1 5 33 5 5 3 4
## 533 534 538 539 540 541 542 543 544 546 548 550 551 552 554
## 2 2 3 9 10 3 2 1 5 6 2 8 2 4 2
## 555 556 558 560 562 564 565 566 567 569 570 572 573 574 575
## 1 2 1 5 1 11 2 2 2 2 2 10 2 2 2
## 576 577 578 582 583 586 588 590 592 594 595 596 598 600 601
## 47 4 3 1 3 2 8 1 2 1 1 2 1 4 1
## 602 603 604 605 606 608 610 611 612 613 614 615 616 617 618
## 1 2 1 1 2 2 2 1 2 1 2 3 1 1 1
## 619 620 621 622 624 625 626 627 628 630 632 636 639 641 642
## 2 1 3 1 7 2 3 1 2 3 3 1 1 1 3
## 644 645 647 648 650 656 660 662 663 665 666 667 668 670 671
## 2 2 1 7 2 3 9 1 1 1 3 1 1 2 1
## 672 673 675 676 678 680 682 683 684 685 686 689 690 691 693
## 15 1 1 5 2 2 1 1 2 1 1 1 1 2 1
## 694 696 701 702 704 706 708 711 712 714 716 719 720 721 722
## 1 1 1 2 1 1 1 1 2 1 1 1 5 1 1
## 726 732 736 738 739 740 746 748 749 750 752 753 754 756 757
## 1 1 3 1 1 1 2 1 1 3 1 1 1 1 1
## 758 765 766 768 770 772 774 776 779 782 784 786 788 789 792
## 3 2 1 1 1 2 2 3 3 1 1 3 1 1 2
## 795 796 800 804 807 808 810 812 813 816 818 820 824 825 826
## 1 2 2 1 1 1 1 2 1 1 1 2 1 1 3
## 830 831 832 833 834 836 839 840 841 842 843 844 846 850 852
## 1 1 1 1 4 3 2 6 1 1 1 1 1 1 1
## 853 856 857 858 860 862 864 865 866 868 870 871 872 874 878
## 1 2 1 1 1 1 2 2 2 2 3 2 1 1 1
## 880 884 888 889 890 894 895 898 900 902 905 908 912 923 924
## 3 3 3 1 1 1 2 1 2 1 1 2 1 1 2
## 928 936 947 954 968 983 995 1014 1020 1025 1043 1052 1053 1069 1134
## 1 2 1 1 2 1 1 1 1 1 1 2 1 1 1
## 1166 1220 1248 1356 1390 1418
## 1 1 1 1 1 1
##
## $GarageQual
##
## Ex Fa Gd Po TA
## 3 48 14 3 1311
##
## $GarageCond
##
## Ex Fa Gd Po TA
## 2 35 9 7 1326
##
## $PavedDrive
##
## N P Y
## 90 30 1340
##
## $SaleType
##
## COD Con ConLD ConLI ConLw CWD New Oth WD
## 43 2 9 5 5 4 122 3 1267
##
## $SaleCondition
##
## Abnorml AdjLand Alloca Family Normal Partial
## 101 4 12 20 1198 125
##
## $SalePrice
##
## 34900 35311 37900 39300 40000 52000 52500 55000 55993 58500
## 1 1 1 1 1 1 1 2 1 1
## 60000 61000 62383 64500 66500 67000 68400 68500 72500 73000
## 3 1 1 1 1 2 1 1 1 1
## 75000 75500 76000 76500 78000 79000 79500 79900 80000 80500
## 1 1 1 1 1 3 1 2 4 1
## 81000 82000 82500 83000 83500 84000 84500 84900 85000 85400
## 3 3 3 2 1 1 3 1 4 1
## 85500 86000 87000 87500 88000 89000 89471 89500 90000 90350
## 1 3 4 1 4 1 1 2 3 1
## 91000 91300 91500 92000 92900 93000 93500 94000 94500 94750
## 3 1 2 1 1 3 2 1 1 1
## 95000 96500 97000 97500 98000 98300 98600 99500 99900 100000
## 2 2 3 1 3 1 1 1 1 9
## 101000 101800 102000 102776 103000 103200 103600 104000 104900 105000
## 1 1 3 1 1 1 1 1 2 5
## 105500 105900 106000 106250 106500 107000 107400 107500 107900 108000
## 1 1 3 1 2 3 1 3 1 6
## 108480 108500 108959 109000 109008 109500 109900 110000 110500 111000
## 1 1 1 2 1 4 3 13 2 1
## 111250 112000 112500 113000 114500 114504 115000 116000 116050 116500
## 1 7 2 6 2 1 12 3 1 1
## 116900 117000 117500 118000 118400 118500 118858 118964 119000 119200
## 1 4 2 6 1 4 1 1 7 1
## 119500 119750 119900 120000 120500 121000 121500 121600 122000 122500
## 4 1 2 7 4 1 1 1 4 2
## 122900 123000 123500 123600 124000 124500 124900 125000 125500 126000
## 1 4 1 1 6 4 2 10 4 3
## 126175 126500 127000 127500 128000 128200 128500 128900 128950 129000
## 1 1 9 6 7 1 4 1 1 8
## 129500 129900 130000 130250 130500 131000 131400 131500 132000 132250
## 4 4 11 1 3 3 1 3 6 1
## 132500 133000 133500 133700 133900 134000 134432 134450 134500 134800
## 6 6 1 1 2 3 1 1 2 1
## 134900 135000 135500 135750 135900 135960 136000 136500 136900 136905
## 2 17 2 1 1 1 3 5 1 1
## 137000 137450 137500 137900 138000 138500 138800 138887 139000 139400
## 5 1 6 1 3 2 1 1 11 2
## 139500 139600 139900 139950 140000 140200 141000 141500 142000 142125
## 1 1 1 1 20 1 8 1 4 1
## 142500 142600 142953 143000 143250 143500 143750 143900 144000 144152
## 3 1 1 10 1 2 1 1 10 1
## 144500 144900 145000 145250 145500 145900 146000 146500 146800 147000
## 2 1 14 1 1 1 3 1 1 9
## 147400 147500 148000 148500 148800 149000 149300 149350 149500 149700
## 1 1 7 3 1 4 1 1 2 1
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## 4 4 1 1 1 5 1 1 6 3
## 153337 153500 153575 153900 154000 154300 154500 154900 155000 155835
## 1 3 1 2 5 1 1 1 14 1
## 155900 156000 156500 156932 157000 157500 157900 158000 158500 158900
## 1 4 1 1 6 2 2 6 1 1
## 159000 159434 159500 159895 159950 160000 160200 161000 161500 161750
## 4 1 3 1 1 12 1 2 2 1
## 162000 162500 162900 163000 163500 163900 163990 164000 164500 164700
## 4 1 2 4 2 1 1 3 2 1
## 164900 164990 165000 165150 165400 165500 165600 166000 167000 167240
## 1 1 8 1 1 3 1 2 4 1
## 167500 167900 168000 168500 169000 169500 169900 169990 170000 171000
## 3 2 4 3 3 2 1 1 8 5
## 171500 171750 171900 172000 172400 172500 172785 173000 173500 173733
## 1 1 1 1 1 5 1 7 1 1
## 173900 174000 174500 174900 175000 175500 175900 176000 176432 176485
## 1 7 1 1 9 3 2 8 1 1
## 176500 177000 177500 178000 178400 178740 178900 179000 179200 179400
## 2 5 3 7 1 1 1 3 2 1
## 179500 179540 179600 179665 179900 180000 180500 181000 181134 181500
## 1 1 1 1 5 10 4 7 1 1
## 181900 182000 182900 183000 183200 183500 183900 184000 184100 184750
## 1 1 1 1 1 1 1 4 1 1
## 184900 185000 185500 185750 185850 185900 186000 186500 186700 187000
## 1 10 1 1 1 1 1 2 1 3
## 187100 187500 187750 188000 188500 188700 189000 189950 190000 191000
## 1 6 1 3 1 1 6 1 13 4
## 192000 192140 192500 193000 193500 193879 194000 194201 194500 194700
## 5 1 2 3 2 1 3 1 3 1
## 195000 195400 196000 196500 197000 197500 197900 198500 198900 199900
## 3 1 3 2 4 2 3 1 1 2
## 200000 200100 200141 200500 200624 201000 201800 202500 202665 202900
## 8 1 1 2 1 3 1 3 1 1
## 203000 204000 204750 204900 205000 205950 206000 206300 206900 207000
## 2 2 1 1 6 1 1 1 1 2
## 207500 208300 208500 208900 209500 210000 211000 212000 212900 213000
## 5 1 1 2 1 5 2 3 1 3
## 213250 213490 213500 214000 214500 214900 215000 215200 216000 216500
## 1 1 2 5 1 1 8 1 1 1
## 216837 217000 217500 218000 219210 219500 220000 221000 221500 222000
## 1 2 1 1 1 3 5 2 1 3
## 222500 223000 223500 224000 224500 224900 225000 226000 226700 227000
## 2 1 2 2 1 2 6 3 1 3
## 227680 227875 228000 228500 228950 229000 229456 230000 230500 231500
## 1 1 2 2 1 1 1 8 1 2
## 232000 232600 233000 233170 233230 234000 235000 235128 236000 236500
## 3 1 1 1 1 2 7 1 2 2
## 237000 237500 238000 239000 239500 239686 239799 239900 240000 241000
## 2 1 1 6 1 1 1 1 6 1
## 241500 242000 243000 244000 244400 244600 245000 245350 245500 246578
## 2 2 1 3 1 1 1 1 1 1
## 248000 248328 248900 249700 250000 250580 251000 252000 252678 253000
## 2 1 1 1 8 1 1 1 1 1
## 253293 254000 254900 255000 255500 255900 256000 256300 257000 257500
## 1 1 1 2 1 1 2 1 1 1
## 258000 259000 259500 260000 260400 261500 262000 262280 262500 263000
## 1 1 1 6 1 1 1 1 2 1
## 263435 264132 264561 265000 265900 265979 266000 266500 267000 268000
## 1 1 1 1 1 1 2 1 1 2
## 269500 269790 270000 271000 271900 272000 274000 274300 274725 274900
## 1 1 3 2 1 2 1 1 1 1
## 274970 275000 275500 276000 277000 277500 278000 279500 280000 281000
## 1 5 1 1 2 1 2 1 4 1
## 281213 282922 283463 284000 285000 286000 287000 287090 289000 290000
## 1 1 1 2 3 1 2 1 1 5
## 293077 294000 295000 295493 297000 299800 301000 301500 302000 303477
## 1 1 1 1 1 1 1 1 2 1
## 305000 305900 306000 307000 309000 310000 311500 311872 312500 313000
## 1 1 1 1 1 2 1 1 1 1
## 314813 315000 315500 315750 316600 317000 318000 318061 319000 319900
## 1 4 1 1 1 1 2 1 1 1
## 320000 324000 325000 325300 325624 326000 328000 328900 333168 335000
## 4 1 4 1 1 1 1 1 1 3
## 336000 337000 337500 339750 340000 341000 342643 345000 348000 350000
## 1 1 1 1 2 1 1 2 1 2
## 354000 359100 360000 361919 367294 369900 370878 372402 372500 374000
## 1 1 1 1 1 1 1 1 1 1
## 375000 377426 377500 378500 380000 381000 383970 385000 386250 392000
## 1 1 1 1 1 1 1 2 1 1
## 392500 394432 394617 395000 395192 402000 402861 403000 410000 412500
## 1 1 1 1 1 1 1 1 1 1
## 415298 423000 424870 426000 430000 437154 438780 440000 446261 451950
## 1 1 1 1 1 1 1 1 1 1
## 465000 466500 475000 485000 501837 538000 555000 556581 582933 611657
## 1 1 1 1 1 1 1 1 1 1
## 625000 745000 755000
## 1 1 1
##
## $SalePriceCat
##
## 1 10 2 3 4 5 6 7 8 9
## 146 145 149 144 150 143 144 146 149 144```## R Markdown